{"action":"created","comment":{"url":"https://api.github.com/repos/mlpack/mlpack/pulls/comments/133326075","pull_request_review_id":56489207,"id":133326075,"diff_hunk":"@@ -0,0 +1,265 @@\n+/**\n+ * @file rbm_network_test.cpp\n+ * @author Kris Singh\n+ *\n+ * Tests the rbm Network\n+ *\n+ * digits dataset source:\n+ * @misc{Lichman:2013 ,\n+ * author = \"M. Lichman\",\n+ * year = \"2013\",\n+ * title = \"{UCI} Machine Learning Repository\",\n+ * url = \"http://archive.ics.uci.edu/ml\",\n+ * institution = \"University of California,\n+ * Irvine, School of Information and Computer Sciences\" } \n+ *\n+ * mlpack is free software; you may redistribute it and/or modify it under the\n+ * terms of the 3-clause BSD license.  You should have received a copy of the\n+ * 3-clause BSD license along with mlpack.  If not, see\n+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.\n+ */\n+#include <mlpack/core.hpp>\n+\n+#include <mlpack/core/optimizers/rmsprop/rmsprop.hpp>\n+#include <mlpack/methods/ann/init_rules/gaussian_init.hpp>\n+#include <mlpack/methods/ann/rbm/binary_rbm_policy.hpp>\n+#include <mlpack/methods/ann/rbm/spike_slab_rbm_policy.hpp>\n+#include <mlpack/methods/ann/rbm.hpp>\n+#include <mlpack/methods/softmax_regression/softmax_regression.hpp>\n+#include <mlpack/core/optimizers/minibatch_sgd/minibatch_sgd.hpp>\n+#include <mlpack/core/optimizers/sgd/sgd.hpp>\n+#include <mlpack/core/optimizers/lbfgs/lbfgs.hpp>\n+\n+#include <boost/test/unit_test.hpp>\n+#include \"test_tools.hpp\"\n+\n+using namespace mlpack;\n+using namespace mlpack::ann;\n+using namespace mlpack::optimization;\n+using namespace mlpack::regression;\n+\n+BOOST_AUTO_TEST_SUITE(RbmNetworkTest);\n+\n+BOOST_AUTO_TEST_CASE(ClassificationTest)\n+{\n+  // Normalised dataset\n+  int hiddenLayerSize = 100;\n+  size_t batchSize = 10;\n+  size_t numEpoches = 30;\n+  arma::mat trainData, testData, dataset;\n+  arma::mat trainLabelsTemp, testLabelsTemp;\n+  trainData.load(\"digits_train.arm\");\n+  testData.load(\"digits_test.arm\");\n+  trainLabelsTemp.load(\"digits_train_label.arm\");\n+  testLabelsTemp.load(\"digits_test_label.arm\");\n+\n+  arma::Row<size_t> trainLabels = arma::zeros<arma::Row<size_t>>(1,\n+      trainLabelsTemp.n_cols);\n+  arma::Row<size_t> testLabels = arma::zeros<arma::Row<size_t>>(1,\n+      testLabelsTemp.n_cols);\n+\n+  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)\n+    trainLabels(i) = arma::as_scalar(trainLabelsTemp.col(i));\n+\n+  for (size_t i = 0; i < testLabelsTemp.n_cols; ++i)\n+    testLabels(i) = arma::as_scalar(testLabelsTemp.col(i));\n+\n+  arma::mat output, XRbm(hiddenLayerSize, trainData.n_cols),\n+      YRbm(hiddenLayerSize, testLabels.n_cols);\n+\n+  XRbm.zeros();\n+  YRbm.zeros();\n+\n+  BinaryRBMPolicy<> binary_rbm(trainData.n_rows, hiddenLayerSize);\n+  GaussianInitialization gaussian(0, 0.1);\n+  RBM<GaussianInitialization, BinaryRBMPolicy<>> model(trainData,\n+      gaussian, binary_rbm, 1,  1, true, false);\n+\n+  size_t numRBMIterations = trainData.n_cols * numEpoches;\n+  numRBMIterations /= batchSize;\n+\n+  MiniBatchSGD msgd(batchSize, 0.06, numRBMIterations, 0, true);\n+  model.Reset();\n+  model.Policy().VisibleBias().ones();\n+  model.Policy().HiddenBias().ones();\n+  // test the reset function\n+  model.Train(trainData, msgd);\n+\n+  for (size_t i = 0; i < trainData.n_cols; i++)\n+  {\n+    model.Policy().HiddenMean(std::move(trainData.col(i)),\n+        std::move(output));\n+    XRbm.col(i) = output;\n+  }\n+\n+  for (size_t i = 0; i < testData.n_cols; i++)\n+  {\n+    model.Policy().HiddenMean(std::move(testData.col(i)),\n+      std::move(output));\n+    YRbm.col(i) = output;\n+  }\n+  const size_t numClasses = 10; // Number of classes.\n+  const size_t numBasis = 5; // Parameter required for L-BFGS algorithm.\n+  const size_t numIterations = 100; // Maximum number of iterations.\n+\n+  // Use an instantiated optimizer for the training.\n+  L_BFGS optimizer(numBasis, numIterations);\n+  SoftmaxRegression regressor2(trainData, trainLabels,\n+      numClasses, 0.001, false, optimizer);\n+\n+  arma::Row<size_t> predictions1, predictions2;\n+  // Vectors to store predictions in.\n+\n+  double classificationAccuray = regressor2.ComputeAccuracy(testData,\n+   testLabels);\n+  std::cout << \"Softmax Accuracy = \" << classificationAccuray << std::endl;\n+\n+  L_BFGS optimizer1(numBasis, numIterations);\n+  SoftmaxRegression regressor1(XRbm, trainLabels, numClasses,\n+        0.001, false, optimizer1);\n+  double classificationAccuray1 = regressor1.ComputeAccuracy(YRbm, testLabels);\n+  std::cout << \"RBM Accuracy = \" <<classificationAccuray1 << std::endl;\n+  BOOST_REQUIRE_GE(classificationAccuray1, classificationAccuray);\n+}\n+\n+BOOST_AUTO_TEST_CASE(ssRBMClassificationTest)\n+{\n+  size_t batchSize = 10;\n+  size_t numEpoches = 30;\n+  int hiddenLayerSize = 100;\n+  double radius = 0;\n+  double tempRadius = 0;\n+  arma::mat trainData, testData, dataset;\n+  arma::mat trainLabelsTemp, testLabelsTemp;\n+  trainData.load(\"digits_train.arm\");\n+  testData.load(\"digits_test.arm\");\n+  trainLabelsTemp.load(\"digits_train_label.arm\");\n+  testLabelsTemp.load(\"digits_test_label.arm\");\n+  GaussianInitialization gaussian(0, 0.1);\n+\n+  arma::Row<size_t> trainLabels = arma::zeros<arma::Row<size_t>>(1,\n+      trainLabelsTemp.n_cols);\n+  arma::Row<size_t> testLabels = arma::zeros<arma::Row<size_t>>(1,\n+      testLabelsTemp.n_cols);\n+\n+  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)\n+    trainLabels(i) = arma::as_scalar(trainLabelsTemp.col(i));\n+\n+  for (size_t i = 0; i < testLabelsTemp.n_cols; ++i)\n+    testLabels(i) = arma::as_scalar(testLabelsTemp.col(i));\n+\n+  for (size_t i = 0; i < trainData.n_cols; i++)\n+  {\n+    tempRadius = arma::norm(trainData.col(i));\n+    if (radius < tempRadius)\n+      radius = tempRadius;\n+  }\n+\n+  size_t poolSize = 1;\n+\n+  arma::mat output;\n+  arma::mat XRbm(hiddenLayerSize + poolSize * hiddenLayerSize,\n+      trainData.n_cols);\n+  arma::mat YRbm(hiddenLayerSize + poolSize * hiddenLayerSize,\n+      testLabels.n_cols);\n+\n+  XRbm.zeros();\n+  YRbm.zeros();\n+  double slabPenalty = 5;\n+\n+  SpikeSlabRBMPolicy<> ss_rbm(trainData.n_rows, hiddenLayerSize, poolSize,\n+      slabPenalty, radius);\n+  RBM<GaussianInitialization, SpikeSlabRBMPolicy<>> modelssRBM(trainData,\n+      gaussian, ss_rbm, 1, 1, true, false);\n+\n+  size_t numRBMIterations = trainData.n_cols * numEpoches;","path":"src/mlpack/tests/rbm_network_test.cpp","position":175,"original_position":175,"commit_id":"06a9756f09e6ad4ff2349d0959d3a243c011a2f6","original_commit_id":"06a9756f09e6ad4ff2349d0959d3a243c011a2f6","user":{"login":"zoq","id":4209744,"avatar_url":"https://avatars3.githubusercontent.com/u/4209744?v=4","gravatar_id":"","url":"https://api.github.com/users/zoq","html_url":"https://github.com/zoq","followers_url":"https://api.github.com/users/zoq/followers","following_url":"https://api.github.com/users/zoq/following{/other_user}","gists_url":"https://api.github.com/users/zoq/gists{/gist_id}","starred_url":"https://api.github.com/users/zoq/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zoq/subscriptions","organizations_url":"https://api.github.com/users/zoq/orgs","repos_url":"https://api.github.com/users/zoq/repos","events_url":"https://api.github.com/users/zoq/events{/privacy}","received_events_url":"https://api.github.com/users/zoq/received_events","type":"User","site_admin":false},"body":"Maybe we decrease the number of epochs to decrease the test time?","created_at":"2017-08-15T23:08:03Z","updated_at":"2017-08-15T23:09:47Z","html_url":"https://github.com/mlpack/mlpack/pull/1046#discussion_r133326075","pull_request_url":"https://api.github.com/repos/mlpack/mlpack/pulls/1046","_links":{"self":{"href":"https://api.github.com/repos/mlpack/mlpack/pulls/comments/133326075"},"html":{"href":"https://github.com/mlpack/mlpack/pull/1046#discussion_r133326075"},"pull_request":{"href":"https://api.github.com/repos/mlpack/mlpack/pulls/1046"}}},"pull_request":{"url":"https://api.github.com/repos/mlpack/mlpack/pulls/1046","id":128634827,"html_url":"https://github.com/mlpack/mlpack/pull/1046","diff_url":"https://github.com/mlpack/mlpack/pull/1046.diff","patch_url":"https://github.com/mlpack/mlpack/pull/1046.patch","issue_url":"https://api.github.com/repos/mlpack/mlpack/issues/1046","number":1046,"state":"open","locked":false,"title":"SpikeSlabRBM","user":{"login":"kris-singh","id":11256139,"avatar_url":"https://avatars1.githubusercontent.com/u/11256139?v=4","gravatar_id":"","url":"https://api.github.com/users/kris-singh","html_url":"https://github.com/kris-singh","followers_url":"https://api.github.com/users/kris-singh/followers","following_url":"https://api.github.com/users/kris-singh/following{/other_user}","gists_url":"https://api.github.com/users/kris-singh/gists{/gist_id}","starred_url":"https://api.github.com/users/kris-singh/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kris-singh/subscriptions","organizations_url":"https://api.github.com/users/kris-singh/orgs","repos_url":"https://api.github.com/users/kris-singh/repos","events_url":"https://api.github.com/users/kris-singh/events{/privacy}","received_events_url":"https://api.github.com/users/kris-singh/received_events","type":"User","site_admin":false},"body":"1)Right now this implements ssRBM layer types.(ssRBM.hpp is not ready i will finish that by tonight or 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